ESTRO 2021 Abstract Book

S978

ESTRO 2021

Seventy patients were analysed, thirty-four affected by COVID-19 pneumonia and thirty-six by radiation therapy-related pneumonitis (RP group). The CT images were quantitatively analyzed by InferRead TM CT Lung (COVID-19) (Infervision, Europe GmbH, Wiesbaden, Germany), an Artificial Intelligence solution specifically developed for diagnosis and management support of COVID-19 pneumonia, based on an AI algorithm built on a novel deep convolutional neural network structure. Based on a preliminary analysis of the deep-learning algorithm, the cut-off value of the estimated risk probability of COVID-19 was set at levels higher than 30% (“COVID19 High Risk”), as the percentage of COVID-19 confirmed patients above this cut-off value was higher than 95%. Values of estimated risk probability below 30% were classified as “COVID19 Low Risk. Results Most patients presenting RP were classified by the algorithm as “COVID19 Low Risk” (66.7%). All RP classified as “COVID19 High Risk” were ≥G3 (CTC AE vers. 4.0). The algorithm showed good accuracy in the detection of RP against COVID-19 pneumonia (sensitivity = 97.0%, specificity = 2%, AUC = 0.72). This accuracy increased when an estimated COVID-19 risk probability cut-off of 30% was applied (sensitivity 76%, specificity 63%, AUC = 0.84). The total lung volume involvement was higher in COVID 19 patients compared with RP group (mean= 105.54 cc, IQ range= 44.68-257.07 vs mean=29.14 cc, IQ range= 5.59-69.20, p <0.001). In patients pretreated with radiation therapy and actually presenting diffuse pneumonitis classified by AI as “COVID19 High Risk” a combination of dosimetric factors may help to identify RP (PPV increased from 60% to 99.8%). Conclusion Deep-learning algorithm can help to discriminate RP from COVID-19 pneumonia, classifying most RP as “Low- risk COVID19” (below the cut off value of COVID-19 risk probability of 30%). In patients classified as high risk , treated with radiation therapy also dosimetric factors should be taken into account. 1 Osmangazi University, Radiation Oncology, Eskisehir, Turkey; 2 Osmangazi University, Radiation Oncology, Eskişehir, Turkey; 3 Osmangazi University, Mathematics and Computer Science, Eskisehir, Turkey; 4 Osmangazi Univsersity, Chest Diseases, Eskisehir, Turkey; 5 Osmangazi University, Chest Diseases, Eskisehir, Turkey Purpose or Objective S mall-cell lung cancer (SCLC) represents about 15% of all lung cancers and is marked by an exceptionally high proliferative rate, strong predilection for early metastasis and poor prognosis. Although multiple treatment modalities are applied the median overall survival (OS) is 16 to 20 months for limited – SCLC (1). A standard treatment based on the TNM staging system may not be suitable for every patient. Identifying patients at high risk of recurrence and high mortality due to the disease is also valuable in guiding treatment. Therefore, in this complex and heterogeneous disease group, it is important to evaluate prognosis in a personalized manner and plan treatment accordingly. The aim of the study is to predict OS with machine learning in limited- SCLC. Materials and Methods The study included 86 cases diagnosed with limited- SCLC from 2007 to 2018. In the prediction of OS, the following 25 variables were evaluated: age, gender, Karnofsky Performance score (KPS), body mass index (BMI), smoking history, presence of chronic obstructive pulmonary disease (COPD), tumor localization, tumor size, lymph node site, lymph node involvement (single level/multilevel), T stage, N stage, TNM stage, presence of concurrent chemotherapy (CT), concurrent CT scheme, number of CT cycles before RT, GTV, PTV, total RT dose, RT fraction dose, prognostic nutritional index (PNI), pretreatment serum albumin and hemoglobin values, neutrophil lymphocyte ratio (NLR), and advanced lung cancer inflammation index (ALI). For the prediction, the ML algorithms of logistic regression, multilayer perceptron classifier (MLP), eXtreme gradient boosting (XGB) classifier, support vector clustering (SVC), random forest classifier (RFC), and Gaussian Naive Bayes (GNB) were used. As training-test data rates, 80%-20% were selected. Results Patient and tumor characteristics are given in Table-1. Median RT dose was 54 (45-64) Gy. Median fractionation dose was 1.8 (2-3) Gy. Concurrent CT was applied to 68 cases, and the most commonly used CT scheme was cisplatin + etoposide. Out of 25 variables,13 variables affecting OS were selected using the permutation feature importance method. Important variables were; gender, PTV, pretreatment serum albumin and hemoglobin values, NLR, BMI, KPS, RT fraction dose, number of CT cycles before RT, T stage, tumor localization, presence of concurrent CT and COPD respectivly. At the median 23-month follow-up, 54 cases died due to cancer. Median OS was 21 (5-125) months. The algorithm with the highest accuracy was found to be SVC (Accuracy rate: 0.88, Confidence Interval: 0.74-1, ROC AUC: 0.83, sensitivity: 92%, specificity :75%). ROC AUC graph is given in Figure-1. PO-1180 Machine Learning to Predict Survival in Small Cell Lung Cancer: A Pilot Study M. Yakar 1 , D. Etiz 2 , O. Celik 3 , A. Ozen 2 , M. Metintas 4 , G. Ak 4 , S. Yılmaz 5 , D. Kutri 1